CSCN8010-Lab_2 (Matplotlib, Seaborn, Plotly)¶

Graph-1 Resourses link for this Matplotlib¶

In [1]:
import matplotlib.pyplot as plt
import numpy as np

from matplotlib import cm

plt.style.use('_mpl-gallery')

# Make data
X = np.arange(-5, 5, 0.25)
Y = np.arange(-5, 5, 0.25)
X, Y = np.meshgrid(X, Y)
R = np.sqrt(X**2 + Y**2)
Z = np.sin(R)

# Plot the surface
fig, ax = plt.subplots(subplot_kw={"projection": "3d"})
ax.plot_surface(X, Y, Z, vmin=Z.min() * 2, cmap=cm.Blues)

ax.set(xticklabels=[],
       yticklabels=[],
       zticklabels=[])

plt.show()

This code produces a 3D plot of the sine function on a grid, resulting in a blue, Wavy surface.

Graph-2 Resourses link for this Seaborn¶

In [2]:
import seaborn as sns
sns.set_theme(style="ticks", palette="pastel")

# Load the example tips dataset
tips = sns.load_dataset("tips")

# Draw a nested boxplot to show bills by day and time
sns.boxplot(x="day", y="total_bill",
            hue="smoker", palette=["m", "g"],
            data=tips)
sns.despine(offset=10, trim=True)

This describes the distribution of total bills across days, differentiated by smoker status, with a pastel color palette and tick styling, using the "tips" dataset.

Graph-3 Resourses link for this Plotly¶

In [3]:
import plotly
plotly.offline.init_notebook_mode()
In [4]:
import plotly.express as px
df = px.data.gapminder()
fig = px.scatter(df.query("year==2007"), x="gdpPercap", y="lifeExp", size="pop", color="continent",
           hover_name="country", log_x=True, size_max=60)
fig.show()

This is used to generate an interactive scatter plot of GDP per capita against life expectancy for the year 2007, with marker size representing population, color indicating continent, and a logarithmic x-axis scale.

TABLE¶

Name Age Occupation Location
Sumit 30 Teacher Waterloo, Canada
Sourabh 20 Computer technician Waterloo, Canada
Paras 19 Student Waterloo, Canada
Lakshay 26 Data Analyst Waterloo, Canada

Image¶

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